Aerial photography, light detection and ranging (“LIDAR”), synthetic aperture radar, and other types of remote sensing technologies are capable of capturing digital imagery of real-world scenes for the purpose of extracting three-dimensional point coordinate (spatial geometry) data. These technologies are widely used in industry as vital tools to collect the data necessary for map-making, engineering modeling, land management, vegetation assessment/management, and/or asset management. These tools are valuable because they can capture spatial (point coordinate) data in a digital form that ultimately allows a wide variety of computer-based tools to be applied to the tasks of map-making, 3D modeling for engineering analysis, vegetation assessment/management, and/or asset management. Today, however, considerable time and effort (manual human intervention) is required to “interpret” the resulting imagery and extract information suitable (e.g., in a more meaningful object-oriented form) for the end-application use of the desired (necessary and sufficient) information. Thus, systems and methods are continuously sought to improve the accuracy, effectiveness, and efficiency of “measurements” which can be directly compared or contrasted against specific criteria to determine the risks associated with any failures to meet/satisfy such specific measurement criteria.
Three-dimensional coordinate point data (3D imagery of a real-world scene) is practically useless unless the location of individual points in 3D space can be compared to the locations of recognizable objects within the real-world scene (e.g., the relative spatial relationships between the recognizable objects and the individual measured points can be compared to specific measurement criteria). The immediate need is to determine whether or not specific clearance distances are maintained between the recognizable objects and the individually measured points on the potential violating object.
There are many tools available to model and analyze the spatial relationships between two objects after they have been recognized and after their geometric and physical attributes have been determined. However, at the present time, there are only a few rudimentary approaches (usually based on simple direct point-to-point distance measurements/calculations) to determine the degree of interference (relative spatial clearance criteria violation) between two objects in 3D space.
The most widely accepted manual approach to dealing with vegetation clearance violations is to “clear-cut” the rights of way of all “significant” vegetation. This is also the most expensive and most conservative approach, but it generally does away with the vegetation risks within the boundaries of the rights of way. Outside the rights of way, taller vegetation (trees) still pose a significant risk, and protracted negotiations (often involving protracted court cases) with land owners are required to gain the right to mitigate such risks. The issue of accuracy of the determination of the risk level is often the key point of the negotiations.
Another widely accepted manual approach to identifying vegetation clearance violations of specific right of way clearance criteria is to: a) have an arborist or forester attempt to visualize where an electrical conductor or other object of interest might physically be/exist under specific operating conditions; and then b) estimate and/or attempt to measure the distance from some vegetation point to an imagined point on a conductor or other object of interest where that particular conductor point might exist under a given operating condition such as conductor operating temperature. Again, the issue of accuracy of the determination of the risk level is often the key point.
One data intensive approach is to have photogrammetrists construct stereo-models from pairs of stereo photographs, either traditional film or scanned digital images, of the right of way having objects of interest. Then, the photogrammetrists use their visual interpretive skills to interpret the images and manually digitize (measure) the distance between selected points on the recognized objects of interest. The measured distance is compared to the required clearance criteria to identify violations. This is an interpretive approach subject to error.
Another data intensive approach is to have photogrammetrists and/or data analysts construct “point cloud” models from either stereo photography or 3D LIDAR/synthetic aperture radar derived points, visually interpret the point clouds, and digitize (measure) the distance between selected points on the recognized objects of interest. Then the measured distance is compared to the required clearance criteria to identify violations. Again, this is an interpretive approach subject to error.
Yet another data intensive approach is to have data analysts construct classified “point cloud” models from 3D LIDAR/synthetic aperture radar derived points. This is done by subdividing or classifying the total set of available points into smaller sets of points with each set being associated with a particular object or type of object and then comparing each point in one selected set of points with each point in each of the remaining sets of points to determine whether or not an interference (clearance violation) exists between any two points of the point sets being compared. The ability of the analyst to visualize the degree of interference (clearance violation) between any two interfering sets of points is difficult at best; while the requirement to communicate the location and degree of interference to others is tedious, laborious, and nearly impossible to resolve, particularly when the possibility of very large numbers of clearance violations readily exists.
Yet another data intensive approach is to have data analysts/engineers construct classified “point cloud” models from 3D LIDAR/synthetic radar derived points (e.g., subdivide or classify the total set of available points into smaller sets of points with each set being associated with a particular object or type of object), construct engineering models from the data, construct the conductor catenary curves for the appropriate conductor operating conditions, and compare the distance from each catenary curve to each vegetation point to the required clearance criteria in order to determine violations of the criteria using automated engineering analysis/design software packages. Although this approach produces accurate and useful results, it requires special 3D engineering model construction and analysis skills to accomplish the task.
Each of the preceding examples of existing available analytical approaches has at least one major shortcoming that has not been dealt with to date. That is, not one of the previously mentioned approaches provides the inspector in the field with a workable tool to accomplish his job after the initial analysis results have been consumed (e.g., the vegetation violations have been cut or trimmed), and/or the vegetation/trees have grown back to a state that the violations have reoccurred. Thus, the capability to audit or check the clearing/cutting work or check for new violations does not exist.
Each of the preceding examples of existing available analytical approaches is directed toward the task of discerning the existence of interferences (clearance violations) between two recognizable objects of interest within the real-world scene of multiple available objects. All of the above methods/approaches have major shortcomings; and few, if any of the methods/approaches mentioned above, lend themselves to being implemented “in the field” or out in the physical real world where the physical objects actually exist. The data processing events and computing power/equipment requirements of the previously mentioned approaches prohibit such in the field execution of such methods/approaches. The data volumes are simply too large to handle easily in the field. Meanwhile, manual methods require gross estimates of changing physical conditions and accuracy is limited, so drastic solutions such as clear-cutting prevail.
Thus there is a real need for a method/system and apparatus that resolves the measurements/results accuracy and reporting issues as well as the data volume issues while providing a solution approach that can be applied equally as effectively in the office using more capable data processing techniques and in the field using small, light weight, portable equipment.